code: def wrap_udf_in_torch_dist_launcher(
      udf_command: str,
      num_trainers: int,
      num_nodes: int,
      node_rank: int,
      master_addr: str,
      master_port: int,
  ) -> str:
      """Wraps the user-defined function (udf_command) with the torch.distributed.run module.
  
       Example: if udf_command is "python3 run/some/trainer.py arg1 arg2", then new_df_command becomes:
           "python3 -m torch.distributed.run <TORCH DIST ARGS> run/some/trainer.py arg1 arg2
  
      udf_command is assumed to consist of pre-commands (optional) followed by the python launcher script (required):
      Examples:
          # simple
          python3.7 path/to/some/trainer.py arg1 arg2
  
          # multi-commands
          (cd some/dir && python3.7 path/to/some/trainer.py arg1 arg2)
  
      IMPORTANT: If udf_command consists of multiple python commands, then this will result in undefined behavior.
  
      Args:
          udf_command:
          num_trainers:
          num_nodes:
          node_rank:
          master_addr:
          master_port:
  
      Returns:
  
      """
      torch_dist_cmd = construct_torch_dist_launcher_cmd(
          num_trainers=num_trainers,
          num_nodes=num_nodes,
          node_rank=node_rank,
          master_addr=master_addr,
          master_port=master_port,
      )
      # Auto-detect the python binary that kicks off the distributed trainer code.
      # Note: This allowlist order matters, this will match with the FIRST matching entry. Thus, please add names to this
      #       from most-specific to least-specific order eg:
      #           (python3.7, python3.8) -> (python3)
      # The allowed python versions are from this: https://www.dgl.ai/pages/start.html
      python_bin_allowlist = (
          "python3.6",
          "python3.7",
          "python3.8",
          "python3.9",
          "python3",
          # for backwards compatibility, accept python2 but technically DGL is a py3 library, so this is not recommended
          "python2.7",
          "python2",
      )
      # If none of the candidate python bins match, then we go with the default `python`
      python_bin = "python"
      for candidate_python_bin in python_bin_allowlist:
          if candidate_python_bin in udf_command:
              python_bin = candidate_python_bin
              break
  
      # transforms the udf_command from:
      #     python path/to/dist_trainer.py arg0 arg1
      # to:
      #     python -m torch.distributed.run [DIST TORCH ARGS] path/to/dist_trainer.py arg0 arg1
      # Note: if there are multiple python commands in `udf_command`, this may do the Wrong Thing, eg launch each
      #       python command within the torch distributed launcher.
      new_udf_command = udf_command.replace(
          python_bin, f"{python_bin} {torch_dist_cmd}"
      )
  
      return new_udf_command
  
imports: import argparse
  import json
  import logging
  import multiprocessing
  import os
  import queue
  import re
  import signal
  import subprocess
  import sys
  import time
  from functools import partial
  from threading import Thread
  from typing import Optional
label_desc: Use image node_id 8 for calling a global function with example usage: wrap_udf_in_torch_dist_launcher(udf_command, num_trainers, num_nodes, node_rank, master_addr, master_port) and returns: new_udf_command